Challenges and Advances in IoT Task Scheduling within Fog Computing: A Review

The rapid expansion of the Internet of Things (IoT) has intensified the demand for efficient task scheduling in fog computing environments, where computational resources are distributed closer to data sources to reduce latency and enhance responsiveness. This paper presents a comprehensive review of task scheduling techniques tailored for fog-enabled IoT systems, focusing on four primary categories: metaheuristic algorithms, heuristic methods, machine learning approaches, and mathematical optimization techniques. We analyze the strengths and limitations of each method in addressing critical challenges such as energy efficiency, latency reduction, deadline adherence, and resource heterogeneity. Furthermore, we identify key research gaps, including the need for multi-objective optimization frameworks that incorporate security, scalability, and real-time adaptability. The paper also outlines promising future directions, emphasizing hybrid models that integrate learning-based and mathematical approaches to improve scheduling performance while ensuring privacy and computational efficiency. This survey aims to provide researchers and practitioners with a clear understanding of the current landscape and inspire innovative solutions for optimizing IoT task scheduling in fog computing.

کلیدواژه ها:

Internet of Things ، IoT ، task scheduling ، fog computing ، metaheuristic algorithms ، heuristic methods ، machine learning ، mathematical optimization ، energy efficiency ، latency reduction ، deadline adherence ، resource heterogeneity ، multi-objective optimization ، security ، scalability ، real-time adaptability ، hybrid models ، privacy ، computational efficiency